Baselines.
We compare with strong state-of-the-art baselines, namely TENT [1]}, BN [2]}, and Prediction Time Normalization (PTN) [3]}.
Note that the results in the TENT paper [1]} are for the online setting over a batch size of 64 and 128 for ImageNet-C and CIFAR-10-C respectively. For reproducing TENT numbers, we optimize the model for 5 iterations at test time with Adam optimizer with a learning rate of 0.001 as it gives the best results within our tested range of \([10^{-2}, 10^{-5}]\) . For BN [2]}, we report results using their recommended parameter setting of \(N=16\) .
